docs: def sg_espcn(tensor, opt):
      r"""Applies a 2-D efficient sub pixel convolution.
         (see [Shi et al. 2016](http://www.cv-foundation.org/openaccess/content_cvpr_2016/papers/Shi_Real-Time_Single_Image_CVPR_2016_paper.pdf)
  
      Args:
        tensor: A 4-D `Tensor` (automatically passed by decorator).
        opt:
          size: A tuple/list of positive integers of length 2 representing `[kernel height, kernel width]`.
            Can be an integer if both values are the same.
            If not specified, (3, 3) is set implicitly.
          stride: A tuple/list of positive integers of length 2 or 4 representing stride dimensions.
            If the length is 2, i.e., (a, b), the stride is `[1, a, b, 1]`.
            If the length is 4, i.e., (a, b, c, d), the stride is `[a, b, c, d]`.
            Can be an integer. If the length is a, the stride is `[1, a, a, 1]`.
            Default value is [1, 1, 1, 1].
          in_dim: A positive `integer`. The size of input dimension.
          dim: A positive `integer`. The size of output dimension.
          pad: Either `SAME` (Default) or `VALID`.
          bias: Boolean. If True, biases are added.
          factor: factor to multiply shape by. Default is 2.
          regularizer:  A (Tensor -> Tensor or None) function; the result of applying it on a newly created variable
            will be added to the collection tf.GraphKeys.REGULARIZATION_LOSSES and can be used for regularization
          summary: If True, summaries are added. The default is True.
  
      Returns:
        A `Tensor` with the same type as `tensor`.
      """
      # default options
      opt += tf.sg_opt(size=(3, 3), stride=(1, 1, 1, 1), pad='SAME', factor=2)
      opt.size = opt.size if isinstance(opt.size, (tuple, list)) else [opt.size, opt.size]
      opt.stride = opt.stride if isinstance(opt.stride, (tuple, list)) else [1, opt.stride, opt.stride, 1]
      opt.stride = [1, opt.stride[0], opt.stride[1], 1] if len(opt.stride) == 2 else opt.stride
  
      # parameter initialize
      w = tf.sg_initializer.he_uniform('W', (opt.size[0], opt.size[1], opt.in_dim, opt.dim * opt.factor * opt.factor),
                                       regularizer=opt.regularizer, summary=opt.summary)
      b = tf.sg_initializer.constant('b', opt.dim, summary=opt.summary) if opt.bias else 0
  
      # apply convolution
      out = tf.nn.conv2d(tensor, w, strides=opt.stride, padding=opt.pad) + b
  
      # apply periodic shuffle
      out = out.sg_periodic_shuffle(factor=opt.factor)
  
      return out